import pandas as pd
import urllib
import numpy as np
import urllib.request
import re
from textblob import TextBlob
%run lib.py

#name="Legally%20Blonde"
#name="aboutmary"
#name="10Things"
name="magnolia"
#name="Friday%20The%2013th"
#name="Ghost%20Ship"
#name="Juno"
#name="Reservoir+Dogs"
#name="shawshank"
#name="Sixth%20Sense,%20The"
#name="sunset_bld_3_21_49"
#name="Titanic"
#name="toy_story"
#name="trainspotting"
#name="transformers"
#name="the-truman-show_shooting"
#name="batman_production"

ext="html"
txtfiles=["Ghost%20Ship", "Legally%20Blonde", "Friday%20The%2013th", "Juno", "Reservoir+Dogs", "Sixth%20Sense,%20The", "Titanic"]
if name in txtfiles:
    ext="txt"
fp = urllib.request.urlopen("http://www.dailyscript.com/scripts/"+name+"."+ext)
mybytes = fp.read()

mystr = mybytes.decode("utf8", "ignore")
fp.close()
liston=mystr.split("\n")
liston=[s.replace('\r', '') for s in liston]
liston=[re.sub('<[^<]+?>', '', text) for text in liston]

if name=="shawshank":
    liston=[i.replace("\t", "    ") for i in liston]

char=""
script=[]
charintro='                                 '
endofdialogue='          '
dialoguepre='                    '
newscenepre='          '
charintro=''
endofdialogue=''
dialoguepre=''
newscenepre=''
i=45
print("Characters")
i, charintro=nextbigchunk(liston, i)
print("Adverbs")
i, adverb=nextbigchunk(liston, i, adverbs=True)
print("Dialogues")
i, dialoguepre=nextbigchunk(liston, i)
print("New Scene:")
i, newscenepre=nextbigchunk(liston, i)

if newscenepre=="X":
    i=100
    i, newscenepre=nextbigchunk(liston, i)
    if name=="aboutmary":
        newscenepre=" ".join(["" for i in range(56)])
    if len(newscenepre)==len(charintro):
        newscenepre="X"
    

endofdialogue=newscenepre
    

scene=1
for s in liston:
    if s[0:len(charintro)]==charintro and s[len(charintro)]!=" " and s.strip()[0]!="(" and s.strip()[len(s.strip())-1]!=")":
        #print("Charatcer*****")
        char=s[len(charintro):]
        new=dict()
        new['char']=char.strip()
        new['dialogue']=""
        new['scene']=scene
        new['adverb']=""
    if s==endofdialogue or s.replace(" ", "")=="":
        if char!="":
            char=""
            script.append(new)
    if char!="" and s[0:len(dialoguepre)]==dialoguepre and s[len(dialoguepre)]!=" ":
        #print("Dialogue******")
        if new['dialogue']!="":
            new['dialogue']=new['dialogue']+" "
        new['dialogue']=new['dialogue']+s[len(dialoguepre):]
    if char!="" and ((s[0:len(adverb)]==adverb and s[len(adverb)]!=" ") or (len(s)>1 and s.strip()[0]=="(" and s.strip()[len(s.strip())-1]==")" )):
        if new['adverb']!="":
            new['adverb']=new['adverb']+" "
        new['adverb']=new['adverb']+s[len(adverb):]
    if s[0:len(newscenepre)]==newscenepre and len(s)>len(newscenepre) and ( s.isupper()) and s[len(newscenepre)]!=" ":
        scene=scene+1

Characters
                                magnolia
                                NARRATOR
                                NARRATOR
                                NARRATOR
                                NARRATOR
                                NARRATOR
Adverbs
Dialogues
                      In the New York Herald, November 26,
                      year 1911, there is an account of the
                      hanging of three men --
                      ...they died for the murder of
                      Sir Edmund William Godfrey --
                      -- Husband, Father, Pharmacist and all
New Scene:
     a P.T. Anderson picture                             11/10/98
     a Joanne Sellar/Ghoulardi Film Company production

pd.DataFrame(script).to_csv(name+'.csv', index=None)
pd.DataFrame(script)

magnolia=pd.read_csv(name+'.csv')
stopwords = getstopwords()

removedchars=["'S VOICE", "'S WHISPER VOICE", " GATOR"]
for s in removedchars:
    magnolia['char']=magnolia['char'].apply(lambda x: x.replace(s, ""))
i=0
scenes=dict()
for s in magnolia.iterrows():
    scenes[s[1]['scene']]=[]
for s in magnolia.iterrows():
    scenes[s[1]['scene']].append(s[1]['char'])
for s in magnolia.iterrows():
    scenes[s[1]['scene']]=list(set(scenes[s[1]['scene']]))

characters=[]
for s in scenes:
    for k in scenes[s]:
        characters.append(k)
characters=list(set(characters))
appearances=dict()
for s in characters:
    appearances[s]=0
for s in magnolia.iterrows():
    appearances[s[1]['char']]=appearances[s[1]['char']]+1

a=pd.DataFrame(appearances, index=[i for i in range(len(appearances))])

finalcharacters=[]
for s in pd.DataFrame(a.transpose()[0].sort_values(0, ascending=False))[0:10].iterrows():
    finalcharacters.append(s[0])

finalcharacters
file=open(name+"_nodes.csv", "w")
couplesappearances=dict()
for s in finalcharacters:
    file.write(";")
    file.write(s)
file.write("\n")
for s in finalcharacters:
    newlist=[]
    for f in finalcharacters:
        newlist.append(0)
        couplesappearances[f+"_"+s]=0
    j=0
    for f in finalcharacters:
        for p in scenes:
            if f in scenes[p] and s in scenes[p] and f!=s and finalcharacters.index(f)<finalcharacters.index(s): 
                long=len(magnolia[magnolia["scene"]==p])
                newlist[j]=newlist[j]+long
                couplesappearances[f+"_"+s]=couplesappearances[f+"_"+s]+long
        j=j+1
    file.write(s)
    for f in newlist:
        file.write(";")
        file.write(str(f))
    file.write("\n")
file.close()

a=pd.DataFrame(couplesappearances, index=[i for i in range(len(couplesappearances))])
finalcouples=[]
for s in pd.DataFrame(a.transpose()[0].sort_values(0, ascending=False))[0:4].iterrows():
    finalcouples.append(s[0])

file=open(name+"_finalcharacters.csv", "w")
for s in finalcharacters:
    file.write(s+"\n")
file.close()
file=open(name+"_finalcouples.csv", "w")
for s in finalcouples:
    file.write(s+"\n")
file.close()

importantchars=[]
for char in appearances:
    if appearances[char]>10:
        importantchars.append(char)

file=open(name+"_sentiment_overtime_individual.csv", "w")
file2=open(name+"_sentiment_overtime_individualminsmaxs.csv", "w")

for k in finalcharacters:
    print(k)
    dd=getdialogue(magnolia, k, k, scenes)
    dd=[str(d) for d in dd]
    polarities, subjectivities=getsentiment(dd)
    %matplotlib inline
    import matplotlib.pyplot as plt
    moveda=maverage(polarities, dd, .99)
    plt.plot(moveda)
    i=0
    for s in moveda:
        file.write(k+","+str(float(i)/len(moveda))+", "+str(s)+"\n")
        i=i+1
    plt.ylabel('polarities')
    plt.show()
    file2.write(k+"| MIN| "+dd[moveda.index(np.min(moveda))]+"\n")
    file2.write(k+"| MAX| "+dd[moveda.index(np.max(moveda))]+"\n")
    print("MIN: "+dd[moveda.index(np.min(moveda))])
    print("\n")
    print("MAX: "+dd[moveda.index(np.max(moveda))])
    
file.close()
file2.close()

file=open(name+"_sentiment_overtime_couples.csv", "w")
file2=open(name+"_sentiment_overtime_couplesminsmaxs.csv", "w")

for k in finalcouples:
    print(k)
    liston=k.split("_")
    dd=getdialogue(magnolia, liston[0], liston[1], scenes)
    dd=[str(d) for d in dd]
    polarities, subjectivities=getsentiment(dd)
    %matplotlib inline
    import matplotlib.pyplot as plt
    moveda=maverage(polarities, dd, .99)
    plt.plot(moveda)
    i=0
    for s in moveda:
        file.write(k+","+str(float(i)/len(moveda))+", "+str(s)+"\n")
        i=i+1
    plt.ylabel('polarities')
    plt.show()
    file2.write(k+"| MIN| "+dd[moveda.index(np.min(moveda))]+"\n")
    file2.write(k+"| MAX| "+dd[moveda.index(np.max(moveda))]+"\n")
    print("MIN: "+dd[moveda.index(np.min(moveda))])
    print("\n")
    print("MAX: "+dd[moveda.index(np.max(moveda))])
    
file.close()
file2.close()

JIM KURRING

MIN: You mind if I check things back here? 


MAX: YOU'RE OK...you're gonna be ok....
JIMMY

MIN: She went crazy.  She went crazy, Rose. 


MAX: Imagine you are attending a jam session of classical composers and they have  each done an arrangment of the classic  favorite, "Whispering."  Here are three  variations on the theme, as three classic  composer's might have written it -- you are to name the composer.  The First: 
CLAUDIA

MIN: I'm sorry. 


MAX: Did you ever go out with someone and just....lie....question after question, maybe you're trying to  make yourself look cool or better  than you are or whatever, or smarter  or cooler and you just -- not really lie, but maybe you just don't say everything --
FRANK

MIN: If you feel, made to feel like you need them, like -- like you can't live if you're without them or you need, what?  They're pussy?  They're love? Fuck that.  Self Sufficient, gents.  That's the truth. What you are -- we are -- you need them  for what?  To fucking make you a piece of snot rag?  A puppett?  huh?  Hear them bitch and moan? bitch and moan --  and we're taught one thing -- go the other way -- there is No Excuse I will give you, I'm not gonna apologize -- I'm not gonna  apologize for my NEED my DESIRE...my, the  things that I need as a man to feel comfortable... You understand?  You understand?  You need to say something, "my mommy hit me or  daddy hit me or didn't let me play soccer,  so now I make mistakes, cause a that -- something, so now I piss and shit on it and do this." Bullshit.  I'm sorry. ok. yeah. no. fuck.  go.  fuck. alright. go make a new mistake. maybe not, I dunno...fuck.... 


MAX: I wouldn't want that to be misunderstood: My enrollment was totally unoffical because I was, sadly, unable to afford tuition up  there.  But there were three wonderful men who were kind enough to let me sit in on their classes, and they're names are:  Macready, Horn and Langtree among others. I was completely independent financially, and like I said: One Sad Sack A Shit.  So what we're looking at here is a true rags to riches story and I think that's  what most people respond to in "Seduce," And At The End Of The Day? Hey -- it may not  even be about picking up chicks and sticking your cock in it -- it's about finding What You Can Be In This World.  Defining It.  Controling It and  saying: I will take what is mine.  You just happen  to get a blow job out of it, then hey-what-the-fuck- why-not?  he.he.he.
PHIL

MIN: You wanna call him on the phone? We can call him, I can dial the  phone if you can remember the number -- 


MAX: Thank you, Chad, and good luck to you and your mother -- 
STANLEY

MIN: I think that you have to be nicer to me.


MAX: I'm fine. I'm fine, I just wanna keep playing --
DONNIE

MIN: My teeff...my teeef....


MAX: My name is Donnie Smith and I have lot's of love to give. 
EARL

MIN: No, no, the grade...the grade that you're in? 


MAX: "...it's not going to stop 'till you wise up..."
LINDA

MIN: listen...listen to me now, Phil:  I'm sorry, sorry I slapped your face.  ...because I don't know what I'm doing... ...I don't know how to do this, y'know?  You understand?  y'know?  I...I'm...I do things  and I fuck up and I fucked up....forgive me, ok? Can you just...


MAX: I'm listening.  I'm getting better. 
NARRATOR

for key, val in scenes.items():
    for s in scenes[key]:
        new="INSCENE_"+scenes[key][0]
        scenes[key].remove(scenes[key][0])
        scenes[key].append(new)

magnolia.dropna(subset=['dialogue'])
1

1

baskets=[]
spchars=["\"", "'", ".", ",", "-"]
attributes=["?", "!"]
for s in magnolia.iterrows():
    if type(s[1]['dialogue'])!=float and  len(s[1]['dialogue'])>0:
        new=[]
        for k in scenes[s[1]['scene']]:
            new.append(k)
        new.append("SPEAKING_"+s[1]['char'])
        for k in s[1]['dialogue'].split(" "):
            ko=k
            for t in spchars:
                ko=ko.replace(t, "")
            for t in attributes:
                if ko.find(t)>=0:
                    new.append(t)
                    ko=ko.replace(t, "")
            if len(ko)>0:
                new.append(ko.lower())
        new=list(set(new))
        baskets.append(new)

baskets2=[]
basketslist=[]
for k in baskets:
    new=dict()
    new2=[]
    for t in k:
        if t not in stopwords:
            new[t]=1
            new2.append(t)
    baskets2.append(new)
    basketslist.append(new2)

baskets2=pd.DataFrame(baskets2)
from mlxtend.frequent_patterns import apriori
from mlxtend.frequent_patterns import association_rules
baskets2=baskets2.fillna(0)
baskets2.to_csv(name+'_basket.csv')

frequent_itemsets = apriori(baskets2, min_support=5/len(baskets2), use_colnames=True)
rules = association_rules(frequent_itemsets, metric="lift", min_threshold=1)

rules['one_lower']=[int(alllower(i) or alllower(j)) for i, j in zip(rules['antecedants'], rules['consequents'])]

rules['both_lower']=[int(alllower(i) and alllower(j)) for i, j in zip(rules['antecedants'], rules['consequents'])]

rules.to_csv(name+'_rules.csv', index=None)

	adverb	char	dialogue	scene
0		magnolia		1
1		NARRATOR	In the New York Herald, November 26, year 1911...	2
2		NARRATOR	...they died for the murder of Sir Edmund Will...	2
3		NARRATOR	-- Husband, Father, Pharmacist and all around ...	2
4		NARRATOR	Greenberry Hill, London. Population as listed.	3
5		NARRATOR	He was murdered by three vagrants whose motive...	5
6		NARRATOR	...Joseph Green.....	5
7		NARRATOR	...Stanley Berry....	5
8		NARRATOR	...and Nigel Hill...	5
9		NARRATOR	Green, Berry and Hill.	7
10		NARRATOR	...And I Would Like To Think This Was Only A M...	7
11		NARRATOR	As reported in the Reno Gazzette, June of 1983...	9
12		NARRATOR	--- the water that it took to contain the fire --	10
13		NARRATOR	-- and a scuba diver named Delmer Darion.	12
14		NARRATOR	Employee of the Peppermill Hotel and Casino, R...	15
15		NARRATOR	-- well liked and well regarded as a physical,...	16
16		NARRATOR	-- as reported by the coroner, Delmer died of ...	21
17		NARRATOR	...volunteer firefighter, estranged father of ...	24
18		NARRATOR	-- added to this, Mr. Hansen's tortured life m...	26
19		CRAIG HANSEN	...oh God...fuck...I'm sorry...I'm sorry...	27
20		NARRATOR	The weight of the guilt and the measure of coi...	27
21		CRAIG HANSEN	...forgive me...	27
22		NARRATOR	And I Am Trying To Think This Was All Only A M...	29
23		NARRATOR	The tale told at a 1961 awards dinner for the ...	32
24		NARRATOR	Seventeen year old Sydney Barringer. In the ci...	33
25		NARRATOR	The coroner ruled that the unsuccessful suicid...	33
26		NARRATOR	The suicide was confirmed by a note, left in t...	34
27		NARRATOR	At the same time young Sydney stood on the le...	35
28		NARRATOR	The neighbors heard, as they usually did, the...	36
29		NARRATOR	-- and it was not uncommon for them to threat...	37
...	...	...	...	...
1493		DIXON	We gotta get his money so we can get outta her...	382
1494		WORM	That idea is over now. We're not gonna do tha...	382
1495	(to Stanley)	DIXON	DADDY, FUCK, DADDY, DON'T GET MAD AT ME. DON'T...	382
1496		WORM	I'm not mad, son, I will not be mad at you an...	382
1497		DIXON	DAD.	382
1498		DIXON	I - just - thought - that - I - didn't want - ...	382
1499		WORM	It's ok, boy.	382
1500		MUSIC/KERMIT THE FROG	"It's not that easy bein' green... Having to s...	383
1501		DONNIE	My teeff...my teeef....	385
1502		JIM KURRING	YOU'RE OK...you're gonna be ok....	385
1503		NARRATOR	And there is the account of the hanging of thr...	390
1504		NARRATOR	There are stories of coincidence and chance an...	391
1505		NARRATOR	...and we generally say, "Well if that was in...	392
1506		DOCTOR	Are you with us? Linda? Is it Linda?	394
1507		NARRATOR	Someone's so and so meet someone else's so and...	395
1508		NARRATOR	And it is in the humble opinion of this narrat...	398
1509		STANLEY	Dad...Dad.	399
1510		STANLEY	You have to be nicer to me, Dad.	399
1511		RICK	Go to bed.	399
1512		STANLEY	I think that you have to be nicer to me.	399
1513		RICK	Go to bed.	399
1514		NARRATOR	...and so it goes and so it goes and the book...	400
1515		MARCIE	I killed him. I killed my husband. He hit my...	401
1516		DONNIE	I know that I did a thtupid thing. Tho-thtupid...	402
1517		DONNIE	I really do hath love to give, I juth don't kn...	402
1518		JIM KURRING	...these security systems can be a real joke. ...	403
1519		DONNIE	....ohh-thur-I-thur-thill....	403
1520		JIM KURRING	You guys make alotta money, huh?	403
1521	(beat)	JIM KURRING	...alot of people think this is just a job tha...	405
1522		END.		406

sentiment	Porcentaje
positive	55.4%
negative	44.6%

sentiment	Porcentaje
positive	19.8%
negative	14.8%
trust	12.1%
joy	11.5%
anticipation	10.5%
fear	6.9%
anger	6.7%
sadness	6.3%
surprise	6.1%
disgust	5.3%

sentiment	Porcentaje
negative	35.9%
positive	35.0%
uncertainty	24.9%
litigious	3.0%
constraining	1.3%

sentiment	Porcentaje
positive	58.5%
negative	41.5%

Analisis de Script Pelicula About Marie

Bernardo Garcia & Eduardo Hidalgo

May 9, 2018

Agenda

Procesamiento de Texto

Ver archivo Python

Analisis de Sentimiento (Pelicula & Personaje)

Score por Pelicula

Score por Personaje

Score por Personaje en el tiempo

Top 10 Personajes

Top 4 Parejas

Reglas de Asociación entre palabras (Market Basket)

Toda la pelicula

Top 10 Personajes

Top 4 Parejas

Analisis de Relaciones entre Personajes (Pagerank)

sentiment	Porcentaje
positive	19.0%
negative	14.7%
trust	12.7%
anticipation	12.2%
joy	11.0%
fear	7.5%
sadness	6.5%
anger	6.2%
surprise	6.0%
disgust	4.2%

sentiment	Porcentaje
positive	22.7%
joy	15.2%
trust	12.5%
anticipation	11.2%
negative	10.7%
surprise	8.3%
sadness	5.9%
fear	5.1%
anger	4.5%
disgust	4.0%

sentiment	Porcentaje
positive	35.3%
uncertainty	35.3%
negative	21.6%
constraining	3.9%
litigious	3.9%

sentiment	Porcentaje
positive	19.4%
negative	15.3%
trust	12.3%
joy	12.0%
anticipation	10.5%
surprise	6.6%
sadness	6.4%
anger	6.1%
disgust	5.9%
fear	5.4%

sentiment	Porcentaje
positive	20.9%
negative	16.6%
trust	13.5%
joy	12.9%
anger	8.0%
anticipation	8.0%
fear	5.5%
surprise	5.5%
sadness	4.9%
disgust	4.3%

sentiment	Porcentaje
positive	20.7%
negative	15.9%
trust	11.0%
fear	9.8%
anger	9.1%
sadness	9.1%
joy	8.5%
anticipation	6.7%
disgust	5.5%
surprise	3.7%

sentiment	Porcentaje
negative	55.6%
positive	22.2%
uncertainty	11.1%
constraining	5.6%
litigious	5.6%

sentiment	Porcentaje
negative	21.1%
fear	12.3%
positive	12.3%
anticipation	10.5%
anger	9.6%
sadness	8.8%
disgust	7.9%
joy	7.0%
surprise	5.3%
trust	5.3%

sentiment	Porcentaje
negative	25.5%
disgust	15.7%
anger	11.8%
fear	11.8%
anticipation	7.8%
joy	5.9%
positive	5.9%
sadness	5.9%
trust	5.9%
surprise	3.9%

sentiment	Porcentaje
positive	31.2%
joy	18.8%
anticipation	12.5%
negative	12.5%
trust	12.5%
sadness	6.2%
surprise	6.2%

sentiment	Porcentaje
anticipation	14.29%
negative	14.29%
positive	14.29%
sadness	14.29%
anger	9.52%
fear	9.52%
trust	9.52%
disgust	4.76%
joy	4.76%
surprise	4.76%

sentiment	Porcentaje
anticipation	25.0%
anger	12.5%
joy	12.5%
negative	12.5%
positive	12.5%
surprise	12.5%
trust	12.5%

sentiment	Porcentaje
positive	34.6%
trust	23.1%
anticipation	15.4%
joy	15.4%
surprise	7.7%
anger	3.8%

Personaje	Min_Max	Dialogo
TED	MIN	You’re Woogie?
TED	MAX	No, it’s an old football injury.
MARY	MIN	But I think I’d be happiest…with you.
MARY	MAX	That’s right. And the good thing is you can do it anywhere.
HEALY	MIN	He’s no friend of mine.
HEALY	MAX	Fine. Fine.
DOM	MIN	Dom Wooganowski. Duh.
DOM	MAX	Hey, look on the bright side–
TUCKER	MIN	I’ve got a friend in the Boston police department. He faxed me this this morning. I’ll just give you the highlights. After a short stint as a petty thief, Patrick R. Healy graduated to armed robbery by the age of fourteen. At sixteen he committed his first murder–a pretty teacher’s aid named Molly Pettygrove. He was incarcerated until age twenty-two when, despite a grim psychological profile, the state was forced to release him. In his mid-twenties and again in his early thirties he was suspected of homicides in the states of Utah and Washington. Unfortunately, the bodies were so badly decomposed that there wasn’t enough evidence to hold him, and on and on and so forth and so on.
TUCKER	MAX	You heard me, goddamnit. I…I love her.
MAGDA	MIN	That’s because there’s a lot of bad people out there. Hey, Puffy tried to warn you about that Steve guy you was seeing–he was a fucking asswipe–but you had to find out for yourself, didn’t you?
MAGDA	MAX	Knock it off, Pollyanna, just ’cause you’re in love doesn’t mean everyone else has to be.
SULLY	MIN	That’s bullshit, man you, uh, you were on the front line. Remember the, uh, malaria the, uh, typhoon fever that vicious strain of genital herpes?
SULLY	MAX	Fuckin’ Patrick Healy, you think your shit don’t stink. Well I got news for you–you’re goddamn right it don’t! How the hell are ya?!
MARY’S MOM	MIN	Charlie, do something.
MARY’S MOM	MAX	Charlie, that’s mean. Come on in, Ted. Don’t listen to Mr. Wise Guy here. He’s a joke a minute.
MARY’S DAD	MIN	What the hell are you doing?!
MARY’S DAD	MAX	How the hell’d you get the beans all the way up top like that?
WARREN	MIN	Huh?
WARREN	MAX	Good, Ted. Piggy back ride?

Parejas	Min_Max	Dialogo
MARY_HEALY	MIN	Oh. So…what brings you down here?
MARY_HEALY	MAX	We just got here thirty seconds ago. Isn’t this stuff great?
TED_MARY	MIN	Hi, Ted.
TED_MARY	MAX	Ted, are you okay?
HEALY_TUCKER	MIN	Dom, you’re pathetic, fucking over your friend Ted like that.
HEALY_TUCKER	MAX	Look, you asked me to follow your girl around, and I did and I started to like her, and then I realized I just couldn’t in good conscience do it.
TED_DOM	MIN	Dom Wooganowski. Duh.
TED_DOM	MAX	Maybe you’re right. I should look on the bright side. I mean, I’ve still got my health… I’m out of here. I’ve got to get up at six a.m. to move my boss’s brother into his apartment.

Numero de Dialogos	Leverage Minimo	Leverage Maximo
7,148	-0.0018	0.0018
112,936	0.0018	0.0054
121,294	0.0054	0.009
39,152	0.009	0.013
4,612	0.013	0.016
4,692	0.016	0.02